Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · physics/0509145

Discrepancies in Southern Hemisphere Mid-latitude Atmospheric Variability of the NCEP-NCAR and ECMWF Reanalyses

A. Dell'Aquila·P. M. Ruti·S. Calmanti·V. Lucarini
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this study we compare the representation of the southern hemisphere midlatitude winter variability in the NCEP-NCAR and ERA40 reanalyses. We use the classical Hayashi spectral technique, recently applied to compare the description of the atmospheric variability in the northern hemisphere on different spectral sub-domains. We test the agreement of the two reanalysis systems in the representation of the atmospheric activity. In the southern hemisphere, even in the satellite period, the assimilated data are relatively scarce, predominately over the oceans, and they provide a weaker constraint to the model dynamics. We find relevant discrepancies in the description of the variability at different spatial and temporal scales. ERA40 is generally characterised by a larger variance, especially in the high frequency spectral region. In the pre-satellite period the discrepancies between the two reanalyses are large and randomly distributed while after the 1979 the discrepancies are systematic. Moreover, a sudden jump in the VTPR period (1973-1978) is observed, mostly in the ERA40 reanalysis. Our results suggest that today we do not have a well-defined picture of the properties of the winter mid-latitude variability in the southern hemisphere to be used in the evaluation of the realism of climate models and demand for an intercomparison study for the assessment of the self-consistency of the IPCC models in the representation of the analysed properties.

Explore related subjects

physics.ao-phphysics.data-anphysics.flu-dynphysics.geo-ph

Keep this discovery

BibTeXRIS

A. Dell'Aquila, P. M. Ruti, S. Calmanti, V. Lucarini. 2006-04-12. Discrepancies in Southern Hemisphere Mid-latitude Atmospheric Variability of the NCEP-NCAR and ECMWF Reanalyses. https://doi.org/10.1007/s00382-005-0048-x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph↗
arXiv2026

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph↗